Freespace detection, also known as traversable area detection, is an essential component of the autonomous driving technology and plays an important role in path planning both in on-road and off-road environments. However, the existing off-road datasets [1]}, [2]}, [3]}, [4]} do not focus on the traversability analysis in off-road environments. As such, there is a need for a dataset focused on freespace detection task in off-road environments. To address this, we present the OFF-Road Freespace Detection (ORFD) dataset, which was collected from the off-road scenes for promoting deep learning research in off-road environments. Taking into account the complexity and diversity of off-road environments and the influence of different light factors and seasonal factors on autonomous driving, we collected the ORFD dataset in different seasons (spring, summer, autumn and winter), at different time (day, evening and night) and in different scenes (such as woodland, farmland, grassland and countryside). The dataset includes a total of \(12,198\)  LiDAR point cloud and RGB image pairs. We synchronized the LiDAR and RGB image data and labeled the freespace in the image plane. Referring to the processing method of the KITTI urban road dataset [5]}, we projected the LiDAR point cloud onto the corresponding RGB image plane, so as to obtain the depth information of the freespace.
